tidyEmoji helps you discover, count, categorise and sentiment-score the emoji in any text column — social-media posts, product reviews, chat logs, survey responses, support tickets — and hands the result back as tidy data frames that drop straight into a tidyverse workflow.
Unicode is awkward to work with and not every code point is an emoji, which makes emoji statistics fiddly. tidyEmoji takes care of that, including grapheme-aware detection so skin-tone modifiers (👍🏽) and multi-person sequences (👨👩👧👦) are treated as a single emoji rather than being split apart.
install.packages("tidyEmoji")
# development version
# install.packages("devtools")
devtools::install_github("PursuitOfDataScience/tidyEmoji")library(tidyEmoji)
library(dplyr)
reviews <- data.frame(text = c("Best purchase ever \U0001f600\U0001f60d",
"It broke after a day \U0001f621",
"Does the job.",
"Wearing my mask \U0001f637\U0001f637",
"Shipped fast \U0001f3c1\U0001f600"))reviews %>% emoji_summary(text) # entries with emoji vs. total
#> # A tibble: 1 × 2
#> emoji_tweets total_tweets
#> <int> <int>
#> 1 4 5
reviews %>% emoji_filter(text) # keep only the rows that have emoji
#> # A tibble: 4 × 1
#> text
#> <chr>
#> 1 Best purchase ever 😀😍
#> 2 It broke after a day 😡
#> 3 Wearing my mask 😷😷
#> 4 Shipped fast 🏁😀reviews %>% emoji_frequency(text) # every emoji, with name + category
#> # A tibble: 5 × 5
#> emoji name shortcode group n
#> <chr> <chr> <chr> <chr> <int>
#> 1 😀 grinning face grinning Smileys & Emotion 2
#> 2 😷 face with medical mask mask Smileys & Emotion 2
#> 3 🏁 chequered flag checkered_flag Flags 1
#> 4 😍 smiling face with heart-eyes heart_eyes Smileys & Emotion 1
#> 5 😡 enraged face rage Smileys & Emotion 1
reviews %>% top_n_emojis(text, n = 3) # just the most frequent
#> # A tibble: 3 × 4
#> emoji_name unicode emoji_category n
#> <chr> <chr> <chr> <int>
#> 1 grinning 😀 Smileys & Emotion 2
#> 2 mask 😷 Smileys & Emotion 2
#> 3 checkered_flag 🏁 Flags 1emoji_tokens() gives one tidy row per emoji occurrence,
with its name, category and sentiment — ready to count, join or
plot.
reviews %>% emoji_tokens(text)
#> # A tibble: 7 × 5
#> text .emoji .emoji_name .emoji_category .emoji_sentiment
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 Best purchase ever 😀😍 😀 grinning face Smileys & Emot… 0.572
#> 2 Best purchase ever 😀😍 😍 smiling face … Smileys & Emot… 0.678
#> 3 It broke after a day 😡 😡 enraged face Smileys & Emot… -0.173
#> 4 Wearing my mask 😷😷 😷 face with med… Smileys & Emot… -0.171
#> 5 Wearing my mask 😷😷 😷 face with med… Smileys & Emot… -0.171
#> 6 Shipped fast 🏁😀 🏁 chequered flag Flags 0.571
#> 7 Shipped fast 🏁😀 😀 grinning face Smileys & Emot… 0.572reviews %>% emoji_categorize(text) # which Unicode categories each row spans
#> # A tibble: 4 × 2
#> text .emoji_category
#> <chr> <chr>
#> 1 Best purchase ever 😀😍 Smileys & Emotion
#> 2 It broke after a day 😡 Smileys & Emotion
#> 3 Wearing my mask 😷😷 Smileys & Emotion
#> 4 Shipped fast 🏁😀 Flags|Smileys & Emotion
reviews %>% emoji_sentiment(text) # mean emoji sentiment per row (-1 to +1)
#> # A tibble: 5 × 3
#> text .emoji_n .emoji_sentiment
#> <chr> <int> <dbl>
#> 1 Best purchase ever 😀😍 2 0.625
#> 2 It broke after a day 😡 1 -0.173
#> 3 Does the job. 0 NA
#> 4 Wearing my mask 😷😷 2 -0.171
#> 5 Shipped fast 🏁😀 2 0.572emoji_sentiment() uses the bundled Emoji
Sentiment Ranking lexicon (Kralj Novak et al., 2015). See the
package vignette for a fuller tour.